mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-14 15:25:15 +02:00
UY dkdtheta
This commit is contained in:
mu
parent
c793e5d916
commit
054b98d55b
1 changed files with 43 additions and 25 deletions
|
|
@ -114,20 +114,28 @@ class ODE_UY(Kernpart):
|
|||
Vu=self.varianceU
|
||||
Vy=self.varianceY
|
||||
|
||||
# kernel for kuu matern3/2
|
||||
kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
|
||||
|
||||
# kernel for kyy
|
||||
k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2
|
||||
k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
|
||||
kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist))
|
||||
|
||||
|
||||
# cross covariance function
|
||||
kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
|
||||
|
||||
# cross covariance kyu
|
||||
kyup = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t>0 kyu
|
||||
kyun = lambda dist:Vu*Vy*(kyu3(dist)) #t<0 kyu
|
||||
|
||||
# cross covariance kuy
|
||||
kuyp = lambda dist:Vu*Vy*(kyu3(dist)) #t>0 kuy
|
||||
kuyn = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t<0 kuy
|
||||
|
||||
|
||||
for i, s1 in enumerate(slices):
|
||||
for j, s2 in enumerate(slices2):
|
||||
for ss1 in s1:
|
||||
|
|
@ -135,12 +143,13 @@ class ODE_UY(Kernpart):
|
|||
if i==0 and j==0:
|
||||
target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
|
||||
elif i==0 and j==1:
|
||||
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[ss1,ss2]) ) )
|
||||
elif i==1 and j==1:
|
||||
target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
|
||||
else:
|
||||
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[ss1,ss2]) ) )
|
||||
|
||||
#KUU = kuu(np.abs(rdist[:iu,:iu]))
|
||||
|
||||
|
|
@ -205,8 +214,8 @@ class ODE_UY(Kernpart):
|
|||
# dk dtheta for UU
|
||||
UUdtheta1 = lambda dist: np.exp(-lu* dist)*dist + (-dist)*np.exp(-lu* dist)*(1+lu*dist)
|
||||
UUdtheta2 = lambda dist: 0
|
||||
UUdvar = lambda dist: (1 + lu *dist) * np.exp(-lu* dist)
|
||||
|
||||
#UUdvar = lambda dist: (1 + lu*dist)*np.exp(-lu*dist)
|
||||
UUdvar = lambda dist: (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
|
||||
|
||||
# dk dtheta for YY
|
||||
|
||||
|
|
@ -241,18 +250,33 @@ class ODE_UY(Kernpart):
|
|||
|
||||
#dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2)
|
||||
|
||||
|
||||
|
||||
# kyy kernel
|
||||
#k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
|
||||
#k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
#k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
|
||||
k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
|
||||
k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
|
||||
#dkdvar = k1+k2+k3
|
||||
|
||||
#cross covariance kernel
|
||||
kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
|
||||
|
||||
# dk dtheta for UY
|
||||
dkcrtheta2 = lambda dist: np.exp(-lu*dist) * ( (-1)*(lu+ly)**(-2)*(1+lu*dist+lu*(lu+ly)**(-1)) + (lu+ly)**(-1)*(-lu)*(lu+ly)**(-2) )
|
||||
dkcrtheta1 = lambda dist: np.exp(-lu*dist)*(lu+ly)**(-1)* ( (-dist)*(1+dist*lu+lu*(lu+ly)**(-1)) - (lu+ly)**(-1)*(1+dist*lu+lu*(lu+ly)**(-1)) +dist+(lu+ly)**(-1)-lu*(lu+ly)**(-2) )
|
||||
#dkuyp dtheta
|
||||
#dkuyp dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
|
||||
#dkuyp dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
|
||||
#dkuyp dVar = k1() + k2()
|
||||
|
||||
|
||||
|
||||
#dkyup dtheta
|
||||
#dkyun dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
|
||||
#dkyun dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
|
||||
#dkyup dVar = k1() + k2() #
|
||||
|
||||
|
||||
|
||||
|
||||
for i, s1 in enumerate(slices):
|
||||
|
|
@ -261,34 +285,28 @@ class ODE_UY(Kernpart):
|
|||
for ss2 in s2:
|
||||
if i==0 and j==0:
|
||||
#target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
|
||||
#dktheta1[ss1,ss2] =
|
||||
#dktheta2[ss1,ss2] =
|
||||
#dkdvar[ss1,ss2] =
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*UUdtheta1(rdist[ss1,ss2])
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*UUdtheta1(np.abs(rdist[ss1,ss2]))
|
||||
dktheta2[ss1,ss2] = 0
|
||||
dkdvar[ss1,ss2] = self.varianceY*UUdvar(rdist[ss1,ss2])
|
||||
dkdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
|
||||
elif i==0 and j==1:
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
#dktheta1[ss1,ss2] =
|
||||
#dktheta2[ss1,ss2] =
|
||||
#dkdvar[ss1,ss2] =
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
|
||||
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
|
||||
dkdvar[ss1,ss2] = k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2]))
|
||||
#dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) )
|
||||
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) )
|
||||
dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyu3(np.abs(rdist[ss1,ss2])) ,k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])) )
|
||||
#stop
|
||||
elif i==1 and j==1:
|
||||
#target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
|
||||
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
|
||||
dkdvar[ss1,ss2] = k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2]))
|
||||
dkdvar[ss1,ss2] = (k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
|
||||
else:
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
#dktheta1[ss1,ss2] =
|
||||
#dktheta2[ss1,ss2] =
|
||||
#dkdvar[ss1,ss2] =
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
|
||||
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
|
||||
dkdvar[ss1,ss2] = k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2]))
|
||||
|
||||
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , dkcrtheta1(np.abs(rdist[ss1,ss2])) )
|
||||
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , dkcrtheta2(np.abs(rdist[ss1,ss2])) )
|
||||
dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])), kyu3(np.abs(rdist[ss1,ss2])) )
|
||||
#stop
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue